choose_lag_fit_algorithm_logistic: choose_lag_fit_algorithm_logistic

In miLAG: Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data

choose_lag_fit_algorithm_logistic

Description

Runs nlsLM/nls algorithms with three different parameter setups to fit the best Logistic model parameters to our data and chooses the best model

Usage

choose_lag_fit_algorithm_logistic(
  gr_curve,
  n0,
  init_gr_rate = init_gr_rate,
  init_K = init_K,
  init_lag = init_lag,
  max_iter = 100,
  lower_bound = c(0, 0, 0)
)

Arguments

gr_curve	data from one specific growth curve with the following columns: LOG10N, t
n0	the initial biomass
init_gr_rate	initial value for the growth rate
init_K	initial value for the saturation parameter K
init_lag	initial value for the lag parameter
max_iter	max. number of iterations; defaults to 100
lower_bound	lower bound for the bounded nls optimization; defaults to 0

Value

the best nls fitting object with parameters fitted to logistic model (lowest Res.Sum Sq provided that all coefficients are nonnegative)

miLAG documentation built on April 3, 2025, 8:09 p.m.